Workshop on Gamification for Information Retrieval (GamifIR 2016)
Room B, Thursday, July 21, 9:00-10:30; 11:00-12:30; 14:00-15:30; 16:00-17:30.
Organizers
Frank Hopfgartner, University of Glasgow, UK
Gabriella Kazai, Lumi Social News, UK
Udo Kruschwitz, University of Essex, UK
Michael Meder, Technische Universität Berlin, Germany
The third workshop on Gamification for Information Retrieval aims for a widening program that addresses a range of emerging challenges and opportunities in the fields of gamification design and related areas. Gamification is a popular methodology describing the trend of applying game design principles and elements, such as feedback loops, points, badges or leader boards in non-gaming environments with several different objectives. Besides just increasing the fun factor, these could be, for example, to achieve more accurate work, better retention rates and more cost effective solutions by relating motivations for participating as more intrinsic than conventional methods. In the context of Information Retrieval (IR), there are various tasks that can benefit from gamification techniques. Think, for example, of the manual annotation of documents in IR evaluation or participation in user studies to tackle interactive IR challenges. Gamification, however, comes with its own challenges and its adoption in IR is still in its infancy.
Workshop on Heterogeneous Information Access (HIA 2016)
Room D, Thursday, July 21, 9:00-10:30; 11:00-12:30; 14:00-15:30; 16:00-17:30.
Organizers
Ke (Adam) Zhou, Yahoo, UK
Yiqun Liu, Tsinghua University, China
Roger Jie Luo, Snapchat, USA
Joemon M. Jose, University of Glasgow, UK
Information access is becoming increasingly heterogeneous. Especially when the user’s information need is for exploratory purpose, returning a set of diverse results from different resources could benefit the user. For example, when a user is planning a trip to China, retrieve and show results from vertical search engines like travel, flight information, map and Q2A sites could satisfy the user’s rich and diverse information need. This heterogeneous search paradigm is useful in many contexts and brings many new challenges.Aggregated search and composite retrieval are two instances of this new search paradigms that aims to be suitable for such information needs. They could also be useful in several other scenarios: a user aims to re-find comprehensive information about his query in his personal search; or a user searches and gathers different nugget information (e.g. an entity) from a set of RDF Web datasets (e.g., DBpedia, IMDB, etc.); or the user search a set of different files in a peer-to-peer online file sharing systems.This is an emerging area as different services provided are becoming more heterogeneous and complex.Therefore, there are a number of directions that might be interesting for both the research and industrial community. How to select the most relevant resources and present them concisely in order to best satisfy the user? How to model the complex user behavior in this search scenario? How can we evaluate the performance of these systems? Those are a few key interesting research questions to study for heterogeneous information access. We would like to create a forum to encourage discussion and exchange of ideas on heterogeneous information access in different contexts.
Workshop on Medical Information Retrieval (MedIR 2016)
Room E, Thursday, July 21, 9:00-10:30; 11:00-12:30; 14:00-15:30; 16:00-17:30.
Organizers
Steven Bedrick, Oregon Health and Science University, USA
Lorraine Goeuriot, Université Grenoble Alpes, France
Gareth Jones, Dublin City University, Ireland
Anastasia Krithara, NCSR Demokritos, Greece
Henning Müller, University of Applied Sciences Western Switzerland
George Paliouras, NCSR Demokritos Greece
Medical information is accessible from diverse sources including the general web, social media, journal articles, and hospital records; users include patients and their families, researchers, practitioners and clinicians. Challenges in medical information retrieval include: diversity of users and user ability; variations in the format, reliability, and quality of biomedical and medical information; the multimedia nature of data; and the need for accuracy and reliability. The objective of this workshop is to provide a forum to enable the progression of research in medical information retrieval to provide enhanced search services for all users with interest in medical information search. This workshop aims to bring together researchers interested in medical information search with the goal of identifying specific research challenges that need to be addressed to advance the state-of-the-art and to foster interdisciplinary collaborations towards the meeting of these challenges. To enable this, we will encourage participation from researchers in all fields related to medical information search including mainstream information retrieval, but also natural language processing, multilingual text processing, and medical image analysis.
Workshop on Neural Information Retrieval (Neu-IR 2016)
Room Pacinotti, Thursday, July 21, 9:00-10:30; 11:00-12:30; 14:00-15:30; 16:00-17:30.
Organizers
Nick Craswell, Microsoft, USA
W. Bruce Croft, University of Massachusetts, USA
Maarten de Rijke, University of Amsterdam, The Netherlands
Jiafeng Guo, Chinese Academy of Sciences, China
Bhaskar Mitra, Microsoft, UK
In recent years, deep neural networks have yielded significant performance improvements on speech recognition and computer vision tasks, as well as led to exciting breakthroughs in novel application areas such as automatic voice translation, image captioning, and conversational agents. Despite demonstrating good performance on natural language processing (NLP) tasks, the performance of deep neural networks on IR tasks has had relatively less scrutiny. The lack of many positive results in the area of information retrieval is partially due to the fact that IR tasks such as ranking are fundamentally different from NLP tasks, but also because the IR and neural network communities are only beginning to focus on the application of these techniques to core information retrieval problems. Given that deep learning has made such a big impact, first on speech processing and computer vision and now, increasingly, also on computational linguistics, it seems clear that deep learning will have a major impact on information retrieval and that this is an ideal time for a workshop in this area. Our focus is on the applicability of deep neural networks to information retrieval: demonstrating performance improvements on public or private information retrieval datasets, identifying key modelling challenges and best practices, and thinking about what deep neural network architectures tell us about information retrieval problems.Neu-IR (pronounced “new IR”) will be a highly interactive full day workshop that will provide a forum for academic and industrial researchers working at the intersection of IR and neural networks. The purpose is to provide an opportunity for people to present new work and early results, compare notes on neural network toolkits, share best practices, and discuss the main challenges facing this line of research.
Workshop on Privacy-Preserving IR (PIR 2016)
Room Galilei, Thursday, July 21, 9:00-10:30; 11:00-12:30; 14:00-15:30; 16:00-17:30.
Organizers
Grace Hui Yang, Georgetown University, USA
Ian Soboroff, NIST, USA
Li Xiong, Emory University, USA
Charles Clarke, University of Waterloo, Canada
Simson Garfinkel, NIST, USA
Information retrieval (IR) and information privacy/security are two fast-growing computer science disciplines. There are many synergies and connections between these two disciplines. However, there have been very limited efforts to connect the two important disciplines. On the other hand, due to lack of mature techniques in privacy-preserving IR, concerns about information privacy and security have become serious obstacles that prevent valuable user data to be used in IR research such as studies on query logs, social media, and medical record retrieval. This year, we propose this workshop again to continue the efforts of connecting the two disciplines of IR and privacy and security. In 2016, the organizers form a team spanning across IR, Privacy, and the government sector. We target on three themes, differential privacy and IR dataset release, privacy in search and browsing, and privacy in social media. We will create panels with researchers from both fields on these three themes, as well as invite industry speakers for real- world challenges.
Workshop on Search as Learning (SAL 2016)
Room Fermi, Thursday, July 21, 9:00-10:30; 11:00-12:30; 14:00-15:30; 16:00-17:30.
Organizers
Jacek Gwizdka, University of Texas Austin, USA
Preben Hansen, Stockholm University, Sweden
Claudia Hauff, Delft University of Technology, The Netherlands
Jiyin He, Centrum Wiskunde & Informatica, The Netherlands
Noriko Kando, National Institute of Informatics, Japan
Search systems to date are viewed more as tools for the retrieval of content to satisfy immediate information needs, than as part of larger complex information environments in which humans learn while interacting with information content. Since users increasingly learn informally while searching as well as use search systems as tools for self-study, there is a growing recognition of the importance to address the challenges of designing, developing, and evaluating search systems (algorithms, interfaces, etc.) that foster discovery and enhance learning outside of formal educational settings.The Search as Learning Workshop aims to flesh out research directions and methodologies, and survey state-of-the-art approaches in this important emerging research area. Further, this workshop wishes to bring together researchers with backgrounds in information science (IS), human computer interaction (HCI), and information retrieval (IR), with the goal of integrating conceptual, experimental, and simulation-based approaches and methodologies from within these different fields, thus allowing the transformation of search systems as isolated information access tools into systems that provide support for learning directly and that consider the broader outcomes of searching beyond a set of search results.
Workshop on Web Question Answering Beyond Factoids (WebQA 2016)
Room C, Thursday, July 21, 9:00-10:30; 11:00-12:30; 14:00-15:30; 16:00-17:30.
Organizers
Eugene Agichtein, Emory University, USA
Charlie Clarke, University of Waterloo, Canada
Lluís Màrquez , Qatar Computing Research Institute, HBKU, Qatar
Alessandro Moschitti, Qatar Computing Research Institute, HBKU, Qatar
Preslav Nakov, Qatar Computing Research Institute, HBKU, Qatar
Idan Szpektor, Yahoo Labs, Israel
Web search engines have made great progress at answering factoid queries. However, they are not well-tailored for managing more complex questions, especially when they require explanation and/or description. WebQA workshop aims at exploring diverse approaches to answering questions on the Web. This year, particular emphasis will be given to Community Question Answering (CQA), where comments by the users engaged in the community can be used to answer new questions. Questions posted on the Web can be short and ambiguous (similarly to Web queries to a search engine). These issues make the WebQA task more challenging than traditional QA, and finding the most effective approaches for it remains an open problem.Unlike the more formal conference format, the aim of this workshop is to bring together researchers in diverse areas working on this problem, including those from NLP, IR, social media and recommender systems communities. This workshop is specifically designed for the SIGIR audience. However, due to its format, its goal, as compared to the main conference, is to conduct a more focused and open discussion, encouraging the presentation of work in progress and late-breaking initial results in Web Question Answering. Both academic and industrial participation will be solicited, including keynotes and invited speakers.